Few-shot learning can be divided into three main categories, including optimization-based method [1]}, hallucination-based method [2]}, and metric-based method [3]}. On the one hand, the optimization-based methods aim to train a quickly adaptive model and quickly adapt to other tasks by fine-tuning [1]}, [5]}. On the other hand, the hallucination-based methods hallucinate the scarce labeled data by synthesizing representations [2]} with Generative Adversarial Networks (GANs) [7]}. Our work is most relevant to the metric-based methods where Vinyals et al. [3]} and Snell et al. [9]} introduce the pairwise (and classwise) metrics to determine the label of the query set according to the support set. Sung et al. [10]} model the non-linear relation between class representations and queries by neural networks. Moreover, cross-domain few-shot learning [11]} argues that the training and testing sets may not lie in the same distribution. Thus, several techniques such as optimal transport [12]}, self-supervised learning [13]}, [14]} have been introduced to relieve the domain shift.
While the domain shift not only occurs between the training set and the testing set but also between the support set and the query set [15]}. In addition, the optimal transport plan can be easily misguided by the perturbation in the images and leads to unacceptable performance.
